package com.wangjie.config;

import com.wangjie.store.MongoChatMemoryStore;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

import java.util.Arrays;
import java.util.List;

@Configuration
public class XiaoZhiAgentConfig {
    @Autowired
    private EmbeddingModel embeddingModel;

    @Autowired
    private EmbeddingStore embeddingStore;

    @Autowired
    private MongoChatMemoryStore mongoChatMemoryStore;

    @Bean
    public ChatMemoryProvider xiaoZhiChatMemoryProvider() {
        return memoryId -> MessageWindowChatMemory.builder()
                .id(memoryId)
                .chatMemoryStore(mongoChatMemoryStore)
                .maxMessages(20)
                .build();
    }

    @Bean
    public ContentRetriever contentRetriever() {
        return EmbeddingStoreContentRetriever
                .builder()
                // 设置用于生成嵌入向量的嵌入模型
                .embeddingModel(embeddingModel)
                // 指定要使用的嵌入存储
                .embeddingStore(embeddingStore)
                // 设置最大检索结果数量, 这里表示最多返回 1 条匹配结果
                .maxResults(1)
                // 设置最小得分阈值, 只有得分大于等于 0.8 的结果才会被返回
                .minScore(0.8)
                .build();
    }

    // @Bean
    public ContentRetriever xiaoZhiContentRetriever() {
        // 使用默认的文档解析器对文档进行解析
        Document document1 = FileSystemDocumentLoader.loadDocument("D:\\code\\java\\langchain4j-demo\\knowledge\\医院信息.md");
        Document document2 = FileSystemDocumentLoader.loadDocument("D:\\code\\java\\langchain4j-demo\\knowledge\\科室信息.md");
        Document document3 = FileSystemDocumentLoader.loadDocument("D:\\code\\java\\langchain4j-demo\\knowledge\\神经内科.md");
        List<Document> documents = Arrays.asList(document1, document2, document3);
        // 使用内存向量存储
        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
        // 使用默认的文档分割器
        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
        // 从嵌入存储(EmbeddingStore)里检索和查询内容相关信息
        return EmbeddingStoreContentRetriever.from(embeddingStore);
    }
}
